ArticleSensors (Basel, Switzerland)2020
Real-Time Detection for Wheat Head Applying Deep Neural Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- A novel method for estrous cycle staging using supervised object detection.NPP - digital psychiatry and neuroscience · 2025Article
- A lightweight wheat ear counting model in UAV images based on improved YOLOv8.Frontiers in plant science · 2025Article
- CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting.Plant phenomics (Washington, D.C.) · 2024Article
- Feature diffusion reconstruction mechanism network for crop spike head detection.Frontiers in plant science · 2024Article
- WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network.Plant methods · 2023Article
- A CNN-LSTM-att hybrid model for classification and evaluation of growth status under drought and heat stress in chinese fir (Cunninghamia lanceolata).Plant methods · 2023Article
- Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis.Plant methods · 2023Article
- Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm.Frontiers in plant science · 2023Article
- Detection method of wheat spike improved YOLOv5s based on the attention mechanism.Frontiers in plant science · 2022Article
- A wheat spike detection method based on Transformer.Frontiers in plant science · 2022Article
- Wheat Ear Recognition Based on RetinaNet and Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning.Sensors (Basel, Switzerland) · 2021Article
- Object Detection Combining CNN and Adaptive Color Prior Features.Sensors (Basel, Switzerland) · 2021Article
- Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods.Plant phenomics (Washington, D.C.) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3's head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.